5 Best Practices for Gaining Attention in the Fragmented Device Environment

This week at Advertising Week in New York City, Skyhook was invited to participate in a panel discussing “The Challenges of the Fragmented Device Environment.” With the landscape of new devices ever growing, and the fragmentation of users’ time and attention across screens intensifies, best practices for the future must be identified.

The Panel was moderated byRob Kramer, GM Mobile at OpenX, and featured panelists Sarah Dale, VP of Digital & Content from The Wall Street Journal, Skyhook’s own VP of Marketing Mike Schneider, and Travis Johnson, the Global Head of Mobile at IPG.

This panel of Industry experts discussed what it takes to succeed in a fragmented device environment – the challenges advertisers, publishers and marketers face and how trust plays a role in success. For those who weren’t able to attend or make the session this year, here’s a deeper dive into some of the key points we made on the topic of the fragmented device environment:

1) Let’s breakdown down the challenges with cross device monetization/attribution from your perspective. What are the biggest challenges and opportunities you face?

Challenges: We look at this question in the context of our app customers, as in-app advertising plays a big part in their monetization strategy. Users have become more sophisticated and so has the technology. The biggest thing we are noticing is that data is now being leveraged as a central part of the design process. Data-driven design is key to providing a vital experience, and building a great is more than just coming up with a great idea and solving for it elegantly. App owners need to evolve continuously and get to know their users so they can anticipate what they want next not only in their road-maps, but in the next moment.

With location and proximity marketing being such crucial themes for this upcoming year, data is playing a big part in app, and user, retention. Knowing location behavior can tell you a lot about your users - by mapping the functionality they use to the places they are in, you can find patterns in how your app is used when a person goes to those locations. You can then use that data to figure out how to design for those places. Adding something like Skyhook Contextcan help simplify this process by geofencing any and all similar places while also triggering place-based experiences when the user goes to certain locations you designate. We call this process Appticipation.

Opportunities: At Skyhook, we’ve actually discovered that anonymous user footprints spark vital user experiences, greater engagement, and more revenue for publishers and apps. Skyhook takes a user's mobile behavior, combined with demographic information to understand a user on a more personal level. As users move through millions of mapped venues, we assign them actionable and anonymous Personas.

For example, footprints that appear frequently in airports and hotels during weekdays, belong to Business Travellers. If they visit a favorite barista every morning, that’s a Coffee Lover. Knowing your users and quantifying their behavior means you can seize the opportunity to deliver dynamic experiences. It means you can monetize your app by giving your advertisers better targeting, and your users better content. Above all, Skyhook lets apps harness the full potential of location with the power of context.

You can create an infinite number of geofences, individually, by brand or by venue category that maps to Google Adwords and IAB taxonomies. You can use this to create dynamic user experiences. For example, a retail app could let your user do more than just shop online with an "in-store" mode. Once the user enters the venue, you can make the app become their shopping list, their loyalty card, or a guide to find products they like on sale.

2) With mobile we now have ability to track users location which can raise the price of an impression in a programmatic transaction however, there are challenges with the accuracy of this data. What can be done on both sides to thwart location based targeting fraud?

Making the assumption that any location is good location proves to be costly for advertisers and a missed opportunity to deliver highly targeted content to their audiences, therefore depleting profitability and performance.Overlooking the accuracy of location data results in advertisers serving the wrong content to the wrong people in the wrong places. Building audience personas with inaccurate location means that advertisers will not truly know their audience and will not be able to provide relevant and timely content.

Exact location yields an increase in the effectiveness of programmatic bidding algorithms and the value of ad inventories by reaching consumers where and when they are in decision-making mode. Tagging inventory with enriched location data like user behavior along with exact location adds a new layer of meaning to ad units.

Location-based ad targeting means more relevant content for consumers, higher ROI for brands, and more revenue for publishers. In order for location data to be actionable, it must be both accurate and precise.

3) As a buyer how do you effectively target your audience as they move from location to location and across multiple devices throughout the day?

One method - at a macro level - is that to see users’ devices moving through any location, and capture this information to build offline behavioral intelligence. We call this story of device behavior a Persona, and Skyhook’s Personas are based on the most accurate and precise location available today. These “living” behavior profiles are algorithmically constructed and constantly updated, allowing brands to identify their audience and target their messaging with ease.

With geofencing and behavioral insights that are rooted in ground-truth location data, this presents the opportunity to surface products that the end user will like.

4) In your perspective does trust matter more in the mobile ecosystem given the abundance of data being passed between all parties?

Absolutely it matters. Again for apps, as part of the First Time User Experience (FTUX), tell them what kind of data you collect and why you collect the data. Transparency coupled with paying off the use of the data with an insanely awesome experience tells them not only to keep location on, but to let it run in the background.

People are starting to wonder about what they aren’t being told. There are all these good things that technology brings, but there’s the growing sense of “what are you doing with stuff I don’t even know that I’ve given you?”

As a maker of digital products, how transparent are you about the information you collect, and how you use it? This transparency is increasingly important. Maybe there’s no need other than “I want to serve you more relevant ads”, but that is a need worth expressing, and users will respond.

Transparency is a big hurdle.Publishers should tell the user not only what information you collect and why, but what happens if you say no. It’s a respectful thing to do, and builds trust.

For advertisers, the difference between intrusive ads and relevant content is context. The more you know about your consumer the more relevant your message is. We are not far from a future in which content is constantly adapted and served based on user context. Armed with contextual data on consumers, publishers can make smarter decisions about what content will most likely interest and engage their readers, help them through the decision-making process, and reduce choices to only those that are relevant. A highly engaged audience means more ad impressions to serve, and a highly segmented audience is an opportunity to monetize with targeted advertising.

5) Do you see one type of cross device model (Deterministic vs Probabilistic) prevailing?

Probabilistic will prevail and is less privacy evasive. According to AdExchanger, deterministic matching may seem like the “better” solution - but users don’t always stay logged in or use the same email address everywhere:

“Probabilistic cross-device matching is achieved by algorithmically analyzing thousands of different anonymous data points – device type, operating system, location data associated with bid requests, time of day and a host of others – to create statistical, aka likely, matches between devices.”